Comparative study of techniques in explainable artificial intelligence based on explainability methods for structured data
Abstract
This article, derived from research on Explainable Artificial Intelligence (XAI) for detecting unusual activities in virtual classrooms, addresses the critical security and academic integrity challenges arising from the university migration to digital environments. Given the infeasibility of manual supervision over the massive volume of data generated, the study employs an experimental and comparative design to evaluate various XAI methods in identifying anomalies such as suspicious logins or bulk downloads. Through a quantitative and qualitative analysis of anonymous interaction logs within simulated scenarios, the research measures both the technical accuracy of the algorithms and the clarity and utility of the explanations they provide to justify their findings. The results reveal an intrinsic tension between predictive efficacy and interpretability, demonstrating that the choice of technique must align with the institutional need to substantiate decisions. Ultimately, the work concludes that implementing XAI is an essential strategic resource for public universities, as it not only strengthens cybersecurity infrastructure but also promotes transparency and ensures trust in virtual educational processes by offering understandable and auditable supervision models.
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Copyright (c) 2026 Fausto Raúl Orozco Lara,Mariela Paola Espinoza Martínez,Mell Naomi Mainato Acosta,Diego Alexander Siguencia Ordóñez
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